Pranamika Pandey | Full Stack Developer

Hi, I'm Pranamika Pandey.

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Self-driven, quick starter, passionate programmer with a curious mind who enjoys solving complex and challenging real-world problems.

About

I am a Bachelors graduate in Electronics and Communication Engineering

Enthusiastic and tech-driven Full Stack Developer with professional experience in Backend Development, Frontend Development and Machine Learning. I am adept in React.js, Node.js, MongoDB, Python, Django, Docker, and MySQL.

Experience

GameStake Ltd

Full Stack Developer

Collaborated with team members to redesign and rebuild platform improving the customer's visit by 35%.
Developed modular REST APIs for multiple portals of the web application.
Improved the load carrying capacity of the web application by redefining the servers and the backend flow.
Tools: React.js, Node.js, MongoDB, AWS, Docker, Bash, Software Testing

Nov 2020 - March 2021 | Remote

GameStake Ltd

Frontend Developer Intern

Developed 50+ product features.
Assessed UX and UI designs for technical feasibility. Recommended solutions for a streamlined, user-friendly interface for designs.
Developed 50+ test plans and test cases, and raised testing efficiency by 31%.
Led the team on product feature improvements with useful recommendations and collaborative debugging.
Tools: Svelte.js, SAAS, Pug.js, HTML5, CSS3, Docker, Mocha.js, Node.js

Sept 2020 - Nov 2020 | Remote

Omviser

Backend Developer Intern

Collaborated with product team members to implement 30+ feature developments.
Developed a cloud-based data visualization and analytics web application aimed at storing and visualizing data-points of each IoT devices.
Designed and implemented a stacking model predicting the degradation of drugs and achieved 75% - 83% accuracy depending on the data for each drug.
Tools: Python, Django, Docker, Django Rest Framework, HTML5, CSS3, Javascript, jQuery, Bootstrap, and Postgresql.

June 2020 - August 2020 | Remote

Bytera Ltd

Data Science and Machine Learning Intern

Designed a Machine Learning pipeline for ranking and filtering posts in a user's feed as per user preferences.
Average rank from the most accurate and efficient method was found to be 3.5. Approx. 600 users of the application found the recommendations to be useful and accurate as per their interests.
Leveraged features include Keywords from user posts, user ratings, hashtags and tags associated with the content, user’s friends, etc.
Tools: Python, Machine Learning, NLP, Scikit-learn, NLTK

May 2020 - June 2020 | Kolkata, India

Projects

                
Instagram Clone
                  Instagram Application
                
AccomplishmentsTools: React, Tailwind CSS, Firebase 
Built login, sign up, dashboard and the user profile page in Instagram Clone.
Register/login to the web app with Email.
Users can follow anyone, toggle like and comment on posts.
Scroll through posts of the followings.

Twitter Bot
                  Twitter Bot built using Tweepy Library
                
                Accomplishments
                Tools: Python, GUI, Tweepy
Incorporated twitter API to build a twitter bot in Python and GUI programming using Tweepy Library.
This application would be used to follow everyone following you, favorite and Retweet a Tweet based on keywords and reply to a user based on a keyword.

Blog Web-App
                  A simple and extensible blog web-app based on Django.
                
AccomplishmentsTools: HTML, CSS, Python, Django
Users can view posts with date and time of creation.
Admin can Add, Delete, Update posts.

Chat Application
                  A simple UI for users to chat by sending text messages to each other.
                
AccomplishmentsReact.js, Koa.js
Random user generation whenever the application is loaded.
Dynamic chat history updates on each text messages for end-users to refer.
Time Stamp (Local Time) attached with each text message.

Bike Sharing Pattern Predictor
                  A Deep Learning project which predicts Bike Sharing Patterns.
                
AccomplishmentsIncorporated Numpy to build a neural network for predicting bike-sharing patterns.
Preprocessed the data from the dataset.
Training loss and validation loss was found to be 0.067 and 0.160 respectively.

Sentiment Analyser
                  A Sentiment Analyser built using RNN
                
AccomplishmentsDesigned a Sentiment Analysis Web Application where the sentiment prediction
model is implemented using PyTorch framework using XGBoost on Amazon SageMaker and trained on IMDB Dataset.
The accuracy score was found to be 0.876.

Snake Game - JavaScript
                  A user interface where users can play Snake Game and check their scores.
                
AccomplishmentsDesigned the algorithm for Snake Game using raw JavaScript, HTML and CSS with
sound effects and thrilling UI/UX.
Total scores display at the end of game.